Optimizing rock fragmentation in open-pit mines through fuzzy intelligent prediction method

Abstract

Blasting is one of the most important steps in mining operation and it directly affects final results (extraction ore body and costs). Various parameters such as rock mass and explosive properties, and blast geometry influence blasting results. A number of effective parameters in fragmentation should be taken into account to design a suitable blasting pattern, reduce the secondary costs and minimize the adverse effects such as flyrock, back break and ground vibration. Fuzzy theory is a widely used technique in many engineering subjects in which there exist concepts of quality and uncertainly. In this study, the information obtained from blasting operation in B anomaly Sangan Iron Mines have been used. In this model, the blasting pattern parameters such as burden, spacing, hole depth, stemming, charging length, ratio of (K/B), number of rows, specific charge and charge per delay ratio were considered as the input parameters in fuzzy model. Then, the results of fuzzy model were compared with statistical models. Finally, the results of the two models produced from mine blasting operation were compared and evaluated with real values. The correlation coefficient index for two models were 97.8% and 72.19%, and the RMSE were 2.613 and 9.18, respectively.